Journal of Gerontological Nursing

Technology Innovations 

Using Mobile Technology to Assess Balance During a Sit-to-Stand Maneuver Among Older Adults With Fall Risk: A Pilot Study

Deanna Gray-Miceli, PhD, GNP-BC, FGSA, FAANP, FNAP, FAAN; Neel Patel, MS, BS; Akshaya Sekar, BS; Matthew Lee Smith, PhD, MPH, CHES

Abstract

Older adults age ≥65 are susceptible to balance impairment with subsequent reduced mobility and increased fall risk. Orthostatic hypotension or blood pressure drop with standing is a treatable condition associated with loss of balance and falls. To understand this phenomenon, the current project used an Android® device attached to participants' center of mass to determine body sway during a simple sit-to-stand maneuver, while researchers assessed participants' sitting and standing blood pressure and symptomology of dizziness. Analysis of study data from two older adult participants are presented illustrating the applicability for future development of a measure to assess balance during simple movements. The next step in the authors' research trajectory is to analyze larger samples using other computations of data provided by the inertial measurement unit sensing technology. [Journal of Gerontological Nursing, 45(10), 18–23.]

Abstract

Older adults age ≥65 are susceptible to balance impairment with subsequent reduced mobility and increased fall risk. Orthostatic hypotension or blood pressure drop with standing is a treatable condition associated with loss of balance and falls. To understand this phenomenon, the current project used an Android® device attached to participants' center of mass to determine body sway during a simple sit-to-stand maneuver, while researchers assessed participants' sitting and standing blood pressure and symptomology of dizziness. Analysis of study data from two older adult participants are presented illustrating the applicability for future development of a measure to assess balance during simple movements. The next step in the authors' research trajectory is to analyze larger samples using other computations of data provided by the inertial measurement unit sensing technology. [Journal of Gerontological Nursing, 45(10), 18–23.]

In the United States, falls with injury disproportionately impact the lives of older adult populations, especially those age ≥85, who experience the greatest share of preventable deaths (Kramarow, Chen, Hedegaard, & Warner, 2015), emergency department (ED) visitations (Caffrey, 2010), and hospital admissions each year. Injuries from falls account for an estimated 40% of potentially preventable ED visits by nursing home (NH) residents (Caffrey, 2010). Studies show frailty increases the risk of falling and is an independent predictor of disability, hospitalization, iatrogenic complications, and mortality (Fried et al., 2001; Kramarow et al., 2015). The fall rate is twice as large in older adult residents in NHs compared to older adults living in the community (Centers for Disease Control and Prevention, 2012).

In the authors' 3-year cohort study of older adult fallers in one NH (77 participants experiencing 193 falls), the relative risk of older adults with modifiable symptoms associated with a preventable fall, such as a slip, trip, loss of balance, or need to urinate, was 1.4 and 1.2, respectively (Gray-Miceli, Ratcliffe, & Johnson, 2010). In addition, the current research has shown that older adults who experience balance problems with standing have increased risk for orthostatic hypotension (p = 0.002) and decreased muscular coordination (p = 0.001) (Gray-Miceli et al., 2016). Thus, older adults represent a vulnerable population with unique health and safety needs related to reducing falls and associated injuries (Harris-Kojetin et al., 2016).

Maintaining safe ambulation is dependent on the ability to balance the body, but as one ages, balance impairments from a multitude of factors, such as medications or disease, increase fall risk (Gray-Miceli, Johnson, & Strumpf, 2005). Although some factors affecting balance are known and treatable (Gray-Miceli et al., 2005), successful management requires quantitative assessment of dynamic balance that is not presently available to clinicians. Current assessments are based on participants' self-report of balance impairment and/or measures of participants' balance performance at one time point (Berg & Norman, 1996; Berg, Wood-Dauphinee, & Williams, 1995; Hendrich, 2016; Tinetti, 1986). Unfortunately, interpretation of data from these balance scales is subject to error bias by raters, and there is little agreement about which parameters should be considered standard or superior in describing balance impairment (Mancini & Horak, 2010; Shah, Aleong, & So, 2016). Notably, continuous measures of balance that incorporate important physiological events, such as orthostatic hypotension, into the balance metric are lacking.

Quantifying balance ability for older adults is essential to prevent falls, especially for older adults who fall as a result of drops in blood pressure (Gray-Miceli, Ratcliffe, Liu, Wantland, & Johnson, 2012) or if they are asymptomatic during these events. The purpose of the current study is to report data on the body sway of older adults during a sit-to-stand movement using mobile technology. The authors sought to determine if symptoms or physiological drops in blood pressure associated with standing resulted in any observable pattern change in the three-dimensional axes of motion (i.e., x axis, y axis, z axis) captured while wearing a tri-axial accelerometer and gyroscope built in a smartphone, which have been used in prior studies to monitor movement and balance (Mancini et al., 2012; Mathie, Coster, Lovell, & Celler, 2004; Moe-Nilssen & Helbostad, 2002; O'Sullivan, Blake, Cunningham, Boyle, & Finucane, 2009).

Institutional Review Board approval was granted for two phases of the current study through Rutgers University. In Phase I, the authors compared older adults with younger adults to validate their metric. In Phase II, once their metric was validated for use, the authors enrolled a sample of older adults from the NH clinical site (2016 to 2017). The current study reports data from Phase II where the authors enrolled four ambulatory older adults, for whom the authors had complete data from two individuals.

Method

Design

An exploratory, descriptive cross-sectional mixed methods design was used to: (a) assess older adults' balance during a sit-to-stand maneuver; and (b) capture motion patterns along with blood pressure and symptom assessment through use of a mobile device (smartphone) worn by older adults. The current study was conducted in one NH in southern New Jersey on the sub-acute rehabilitation unit located within the facility. The sub-acute rehabilitation unit has 100 beds and a full-service physical therapy department.

Sample

A convenience sample of four older adults was recruited, screened for eligibility, and enrolled in the current study. Eligibility requirements included: age ≥65, English speaking, cognitively intact, receiving physical therapy in the sub-acute unit, ability to move independently with minimal assistance of one person or by use of the arm of a chair from a sitting to standing position, free of active cancer diagnosis, and no medical contra-indications to performing the sit-to-stand maneuver.

Measures

Demographic Questionnaire. Participants completed a demographic questionnaire that included open-ended and force-choice questions about age, height, weight, medical diagnosis, recent fall to the ground, and current medical issues (e.g., chest pain, joint pain, medical clearance related to exercise clearance). Medical history data included: visual or hearing impairment, presence of cancer, high blood pressure, blood pressure drops with standing, dizziness or lightheadedness at any time, coronary artery disease (CAD), stroke, arthritis, osteoporosis, recent fractures, or Parkinson's disease.

Blood Pressure Recordings. Blood pressure was manually obtained using a portable handheld sphygmomanometer. Readings were obtained in the right arm (unless contraindicated) at rest, while sitting in the armchair and then repeated 30 seconds later when standing. If drops in blood pressure occurred (from sitting to standing), participants were asked if they felt any symptoms. If so, they were eased to a sitting position immediately. If participants had no symptoms, but blood pressure drops were observed to be significant, participants were eased to a sitting position and only one reading using the smartphone recording was obtained.

Quantification of Body Sway. During the sit-to-stand maneuver, body accelerations of each participant's torso in the frontal (x axis), horizontal (y axis), and sagittal (z axis) planes were registered with an inertial measurement unit (IMU) integral with the smartphone (Android®). The IMU software, SensorLog, was provided by Nian-Crae, Inc. Recording commenced at the onset of standing and ended after a full upright stance was achieved (Figure 1).

Schematic of the x-, y-, and z-axes of orientation during the sit-to-stand maneuver.

Figure 1.

Schematic of the x-, y-, and z-axes of orientation during the sit-to-stand maneuver.

Falls Symptoms Questionnaire. Symptoms experienced during the sit-to-stand maneuver were reviewed immediately with each participant after completion. The Falls Symptoms Questionnaire comprises three forced-choice questions related to older adults' sensation of: (a) feeling different or unusual; (b) experiencing lightheadedness; or (c) experiencing dizziness during the sit-to-stand activity. If participants responded positively, they were asked to rate on a scale of 0 to 10 the intensity of the feeling and the degree of distress caused. Anchors for this scale were no distress (0) and a great deal of distress (10).

Activities-Specific Balance Confidence Scale. The Activities-Specific Balance Confidence (ABC) Scale is an 11-point scale designed to measure the level of confidence a person has in doing an activity without losing his/her balance or becoming unsteady (Powell & Meyers, 1995). Each item on the ABC is rated by participants using whole numbers from a possible range of 0 to 100; total possible scores are 0 to 1,100. In prior studies, discriminative and evaluative properties of the ABC scale have been described. Persons scoring ≥80% have high-level physical functioning, those scoring 50% to 80% have moderate physical functioning, and those ≤50% have low physical functioning (Meyers, Fletcher, Myers, & Sherk, 1998). In other studies, a cut-off score of 67% was predictive of future falls (Lajoie & Gallagher, 2004).

Falls Efficacy Scale. The Falls Efficacy Scale (FES) is a 10-item scale that measures the degree of confidence a person has in performing an activity without falling (Tinetti, Richman, & Powell, 1990). In prior studies, the FES has been shown as a valid measure of fear of falling, with a total score ≥70 indicating that a person has a fear of falling.

Protocol

Basic demographic, fall history, blood pressure recording, and symptoms during sit-to-stand were elicited from participants using a survey and hand recordings of blood pressure measurement. Acceleration and angular displacement patterns were determined by use of a tri-axial accelerometer and gyroscope built within the smartphone. Following informed consent, participants were screened for cognitive status using the Mini-Mental State Examination test (Folstein, Folstein, & McHugh, 1975). Participants deemed cognitively intact were enrolled, asked to complete the demographic questionnaire, and given a pictorial illustration of the sit-to-stand maneuver to perform. Participants had their seated blood pressure recorded. Two smart-phones were placed on participants' bodies; one placed on their sternum and held with a snuggly fitted strap and one placed on their lower back and a pouch to hold the smartphone.

Once the smartphone was deemed secure and the sitting blood pressure was taken, participants were asked to stand for 30 seconds. Contact guarding was provided by the study team. On the count of 3, the smartphone was activated, and participants stood up. Blood pressure was measured again at 30 seconds while standing and recorded, then participants were assisted to a seated position. During the entire sit-to-stand maneuver, participants were constantly asked if they experienced any unusual symptoms and to sit down if they expressed lightheadedness while standing. Following the sit-to-stand maneuver, participants completed the Symptom Experience Questionnaire, the ABC Scale, and the FES. Inertial data from the 3-axis accelerometer and 3-axis gyroscope were registered and stored in the smartphone during trials. The sampling rate was 17 samples per second and 80 samples per second for the front and rear smartphones, respectively. Participants followed the instruction “on the count of 3, stand up,” at which time researchers activated the anterior and posterior IMUs for simultaneous data recording from sitting to an upright standing position. Participants maintained standing for 30 seconds (Figure 1, Table 1).

Summary of Study Metrics Relative to Patient's Body Position

Table 1:

Summary of Study Metrics Relative to Patient's Body Position

Results

Acceleration Plots

Figure 2 shows representative plots of x-, y-, and z-axis accelerations of two participants during a single sitto-stand maneuver. Abrupt changes in acceleration in both axes at the onset of the stand action were noted (time 0). Large oscillatory accelerations were noted in both axes during the standing maneuver, which lasted approximately 13 seconds. These accelerations subsequently dissipated during relatively stable standing. The steady baseline shifts, most prominent in the z-axis, were due to the reorientation of the body with respect to gravity from sitting to standing.

X-axis (A), y-axis (B), and z-axis (C) front acceleration of patients A and B during the sit-to-stand maneuver.

Figure 2.

X-axis (A), y-axis (B), and z-axis (C) front acceleration of patients A and B during the sit-to-stand maneuver.

Participant A. Participant A (male, age 81) had a clinically significant history of CAD and osteoarthritis, but no self-reported falls in the 3 months prior to the current study. During the sit-to-stand maneuver, he used the arm rails on the chair to steady himself as he assumed a standing position. He experienced no symptoms during this maneuver and had no significant drop in blood pressure indicative of orthostatic or postural hypotension. Systolic blood pressure while sitting was 146 mmHg, which dropped to 139 mmHg with standing. FES score was nonsignificant (score = 10 of 100).

Participant B. Participant B (male, age 86) had a clinically significant history of hypertension and chronic obstructive pulmonary disease, fracture injury due to a fall, osteoarthritis, visual and hearing impairment, and self-reported a fall 3 months prior to the current study. During the sit-to-stand maneuver, he used the arm rails and experienced a significant 33 mmHg drop in blood pressure from sitting to standing, during which time he experienced a sensation of dizziness. At the onset of dizziness, the smartphone three-dimensional recording was stopped, and the participant was eased to a sitting position by the study team. The FES score was nonsignificant (score = 10 of 100).

Discussion

Using a smartphone, the authors showed the utility of biomechanical behavior during the everyday sit-to-stand maneuver, a metric of balance. To the authors' knowledge, this is one of the first studies to examine the three-dimensional axes of motion among a sample of older adults who have fallen, complemented by physically examined drops in blood pressure to observe real-time pattern changes. Using two participants, the current pilot study demonstrated a simple procedure to quantify balance with a smartphone to collect raw data. Clinical research evidence has also shown use of accelerometers to measure standing balance and quantify human movement to be cost effective and an accessible alternative to other traditional measures (Mathie et al., 2004; Moe-Nilssen & Helbostad, 2002). Using the smartphone application worn by participants, the authors could measure vectors of motion, such as acceleration, azimuth, pitch, and roll, which correlate to the body's movement in space and time. Additional studies that add the use of a camera would be beneficial to data interpretation and refinement of the data analysis.

Limitations

There were a few limitations to the current study. The authors had no measure to visually analyze body characteristics, such as slumping or back and forth swaying during the smartphone testing. More controlled measures of analyzing balance, such as use of a GoPro® or other video recording and/or a 10-second lead-in before and after the maneuver could help detect artifact in the motion data, which would be valuable in a laboratory setting. Another major limitation is that the current study was conducted in a natural environment setting. The authors used the smartphone application to assess balance in a private room at the long-term care settings. The benefits of doing this testing in this setting speak to minimization of “being watched” or “tested” in a laboratory environment, which could be an important contextual factor in studying balance performance.

Conclusion

The authors' technology has the potential to be useful to clinicians in practice to capture real time changes in body sway, otherwise undetected by clinicians using standard balance measures. Evidence about excessive sway would offer an important explanatory causal event reason for a patient fall, which would require further assessment and better inform the plan of care for safe mobility. Mobile technology such as this can advance public health innovations to adequately screen for falls in diverse clinical and community settings among at-risk and vulnerable populations, such as older adults (including those residing in long-term care). Such mobile technology applications show great potential to enhance virtual rehabilitation (Goncalves et al., 2018) and quality interactions with health care professionals during in-office fall screening (Stevens, Smith, Parker, Jiang, & Floyd, 2017).

Furthermore, in recent years face-to-face fall prevention and management interventions have been translated into digital platforms to encourage physical activity and monitor gait and balance outside clinical settings (Shubert et al., 2018). Although such programmatic translations can increase the proportion of at-risk older adults reached with interventions to prevent falls and reduce fall-related risk (Smith et al., 2018), smartphone applications can be integrated to increase the accuracy of risk identification, promote self-management strategies among participants, and transmit real-time data to health care professionals working with older adults.

References

  • Berg, K. & Norman, K.E. (1996). Functional assessment of balance and gait. Clinical Geriatric Medicine, 12, 705–723. doi:10.1016/S0749-0690(18)30197-6 [CrossRef]
  • Berg, K., Wood-Dauphinee, S. & Williams, J.I. (1995). The balance scale: Reliability assessment with elderly residents and patients with an acute stroke. Scandinavian Journal of Rehabilitation Medicine, 27, 27–36.7792547
  • Caffrey, C. (2010). Potentially preventable emergency department visits by nursing home residents: United States, 2004. NCHS Data Brief, 33, 1–8.
  • Centers for Disease Control and Prevention. (2012). Falls in nursing homes. Retrieved from https://www.in.gov/isdh/files/CDC_Falls_in_Nursing_Homes.pdf
  • Folstein, M.F., Folstein, S.E. & McHugh, P.R. (1975). “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 13, 189–198. doi:10.1016/0022-3956(75)90026-6 [CrossRef]
  • Fried, L.P., Tangen, C.M., Walston, J., Newman, A.B., Hirsch, C., Gottdiener, J. & McBurnie, M.A. (2001). Frailty of older adults: Evidence for a phenotype. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 56, M146–M156. doi:10.1093/gerona/56.3.M146 [CrossRef]
  • Goncalves Da Silva, M., Albiol-Pérez, S., López Lombano, J., Valdivia Salas, S., Cano, S., Gutierrez, E.G. & Collazos, C.A. (2018). A groundbreaking technology in virtual rehabilitation to improve falls in older people. In Rocha, Á., Adeli, H., Reis, L.P. & Costanzo, S. (Eds.), Trends and advances in information systems and technologies (Vol. 2) (pp. 1251–1261). Basel, Switzerland: Springer. doi:10.1007/978-3-319-77712-2_120 [CrossRef]
  • Gray-Miceli, D., Johnson, J. & Strumpf, N. (2005). A stepwise approach to a comprehensive post-fall assessment. Annals of Long-Term Care, 13, 16–24.
  • Gray-Miceli, D., Ratcliffe, S.J. & Johnson, J. (2010). Use of a postfall assessment tool to prevent falls. Western Journal of Nursing Research, 32, 932–948. doi:10.1177/0193945910370697 [CrossRef]20705774
  • Gray-Miceli, D., Ratcliffe, S.J., Liu, S., Wantland, D. & Johnson, J. (2012). Orthostatic hypotension in elderly nursing home residents who fall: Are they dizzy?Clinical Nursing Research, 21, 64–78. doi:10.1177/1054773811434045 [CrossRef]
  • Gray-Miceli, D., Ratcliffe, S.J., Thomasson, A., Quigley, P.Q., Li, K. & Craelius, W. (2016). Clinical risk factors for orthostatic hypotension: Results among elderly fallers in long-term care. Journal of Patient Safety. Advance online publication. doi:10.1097/PTS.0000000000000274 [CrossRef]27768653
  • Harris-Kojetin, L., Sengupta, M., Park-Lee, E., Valverde, R., Caffrey, C., Rome, V. & Lendon, J. (2016). Long-term care providers and services users in the United States: Data from the National Study of long-term care providers, 2013–2014. Vital & Health Statistics. Series 3, Analytical and Epidemiological Studies, 38, 1–105.
  • Hendrich, A. (2016). Fall risk assessment for older adults: The Hendrich II fall risk model. Retrieved from https://consultgeri.org/try-this/general-assessment/issue-8.pdf.
  • Kramarow, E., Chen, L.H., Hedegaard, H. & Warner, M. (2015). Deaths from unintentional injury among adults aged 65 and over: United States, 2000–2013. NCHS Data Brief, 199, 1–8.
  • Lajoie, Y. & Gallagher, S.P. (2004). Predicting falls within the elderly community: Comparison of postural sway, reaction time, the berg balance scale and the activities-specific balance confidence (ABC) scale for comparing fallers and non-fallers. Archives of Gerontology and Geriatrics, 38, 11–26. doi:10.1016/S0167-4943(03)00082-7 [CrossRef]
  • Mancini, M. & Horak, F.B. (2010). The relevance of clinical balance assessment tools to differentiate balance deficits. European Journal of Physical and Rehabilitation Medicine, 46, 239–248.20485226
  • Mancini, M., Salarian, A., Carlson-Kuhta, P., Zampieri, C., King, L., Chiari, L. & Horak, F.B. (2012). ISway: A sensitive, valid and reliable measure of postural control. Journal of Neuroengineering and Rehabilitation, 9, 59. doi:10.1186/1743-0003-9-59 [CrossRef]22913719
  • Mathie, M.J., Coster, A.C., Lovell, N.H. & Celler, B.G. (2004). Accelerometry: Providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement, 25, R1–R20. doi:10.1088/0967-3334/25/2/R01 [CrossRef]15132305
  • Meyers, A.M., Fletcher, P.C., Myers, A.H. & Sherk, W. (1998). Discriminative and evaluative properties of the activities-specific balance confidence (ABC) scale. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 53, M287–M294. doi:10.1093/gerona/53A.4.M287 [CrossRef]
  • Moe-Nilssen, R. & Helbostad, J.L. (2002). Truck accelerometry as a measure of balance control during quiet standing. Gait & Posture, 16, 60–68. doi:10.1016/S0966-6362(01)00200-4 [CrossRef]
  • O'Sullivan, M., Blake, C., Cunningham, C., Boyle, G. & Finucane, C. (2009). Correlation of accelerometry with clinical balance tests in older fallers and non-fallers. Age & Ageing, 38, 308–313. doi:10.1093/ageing/afp009 [CrossRef]
  • Powell, L.E. & Meyers, A.M. (1995). The activities specific balance confidence (ABC) scale. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 50, M28–M34. doi:10.1093/gerona/50A.1.M28 [CrossRef]
  • Shah, N., Aleong, R. & So, I. (2016). Novel use of a smartphone to measure standing balance. JMIR Rehabilitation & Assistive Technologies, 3, e4. doi:10.2196/rehab.4511 [CrossRef]
  • Shubert, T.E., Chokshi, A., Mendes, V.M., Grier, S., Buchanan, H., Basnett, J. & Smith, M.L. (2018). Stand tall: A virtual translation of the Otago Exercise Program. Journal of Geriatric Physical Therapy. Advance online publication. doi:. doi:10.1519/JPT.0000000000000203 [CrossRef]
  • Smith, M.L., Towne, S.D., Herrera-Venson, A., Cameron, K., Horel, S.A., Ory, M.G. & Skowronski, S. (2018). Delivery of fall prevention interventions for at-risk older adults in rural areas: Findings from a national dissemination. International Journal of Environmental Research and Public Health, 15, 2798. doi:10.3390/ijerph15122798 [CrossRef]
  • Stevens, J.A., Smith, M.L., Parker, E.M., Jiang, L. & Floyd, F.D. (2017). Implementing a clinically based fall prevention program. American Journal of Lifestyle Medicine, 20, 10.
  • Tinetti, M., Richman, D. & Powell, L. (1990). Falls efficacy as a measure of fear of falling. Journal of Gerontology, 45, P239–P243. doi:10.1093/geronj/45.6.P239 [CrossRef]2229948
  • Tinetti, M.E. (1986). Performance-oriented assessment of mobility problems in elderly persons. Journal of the American Geriatrics Society, 34, 119–126. doi:10.1111/j.1532-5415.1986.tb05480.x [CrossRef]

Summary of Study Metrics Relative to Patient's Body Position

MetricSittingRising to StandStanding per 30 SecondsSitting Back Down in Chair
Demographic survey
Blood pressure
Practitioner elicitation of patient symptoms at time of movement
Falls Symptoms Questionnaire
Falls Efficacy Scale
Activities-Specific Balance Confidence Scale
X-axis acceleration
Y-axis acceleration
Z-axis acceleration
Authors

Dr. Gray-Miceli is Associate Professor, Faculty Fellow, Christine E. Lynn College of Nursing, The Institute for Sensing, Embedded Network Systems Engineering (I-SENSE), Florida Atlantic University, Boca Raton, Florida, and former Faculty Member, Rutgers University, College of Nursing, Newark, New Jersey; Mr. Patel is Graduate, Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, Illinois, and R & D Engineer, Johnson and Johnson Ethicon, Somerville, New Jersey; Ms. Sekar is Former Electrical, Computer Engineering and Biological Sciences Student, Rutgers University, Piscataway, New Jersey, and First Year Student, Campbell University, Jerry M. Wallace School of Osteopathic Medicine, Lillington North Carolina; and Dr. Smith is Associate Professor, Codirector, Texas A&M Center for Population Health and Aging, Texas A&M School of Public Health, University of Georgia College of Public Health, College Station, Texas. Mr. Patel is also former BioMedical Engineering Student, Rutgers University, Piscataway, New Jersey.

The authors have disclosed no potential conflicts of interest, financial or otherwise. The current study was supported by the Aresty Student Research Center at Rutgers University, New Brunswick campus, Piscataway, NJ; the work was performed while on the faculty (D.G.M.) at Rutgers University. The authors acknowledge the research assistance provided by Jeremy Stein, Rutgers University undergraduate student, and express their thanks to Dr. William Craelius, Professor of Biomedical Engineering, Rutgers University, School of Engineering, for his significant contributions to data analysis and editorial assistance. The current study would not have been possible without use of the Sensory LogTM from developer, Dr. Nicky Newby, Nian-Crae, Inc.

Address correspondence to Deanna Gray-Miceli, PhD, GNP-BC, FGSA, FAANP, FNAP, FAAN, Associate Professor, Faculty Fellow, Christine E. Lynn College of Nursing, The Institute for Sensing, Embedded Network Systems Engineering (I-SENSE), Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431; e-mail: dgraymiceli@fau.edu.

10.3928/00989134-20190825-01

Sign up to receive

Journal E-contents